Daniele Manerba

Bio: Daniele Manerba is an academic researcher from University of Brescia. The author has contributed to research in topics: Heuristic (computer science) & Traveling purchaser problem. The author has an hindex of 15, co-authored 41 publications receiving 598 citations. Previous affiliations of Daniele Manerba include Polytechnic University of Turin.

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Abstract: As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform.

A Virtual Power Plant Architecture for the Demand-Side Management of Smart Prosumers

Abstract: In this paper, we present a conceptual study on a Virtual Power Plant (VPP) architecture for the optimal management of Distributed Energy Resources (DERs) owned by prosumers participating in Demand-Side Management (DSM) programs. Compared to classical VPP architectures, which aim to aggregate several DERs dispersed throughout the electrical grid, in the proposed VPP architecture the supervised physical domain is limited to single users, i.e., to single Points of Delivery (PODs) of the distribution network. The VPP architecture is based on a service-oriented approach, where multiple agents cooperate to implement the optimal management of the prosumer’s assets, by also considering different forms of Demand Response (DR) requests. The considered DR schemes range from Price-Based DRs to Event-Based DRs, covering both the normal operating functions and the emergency control requests applied in modern distribution networks. With respect to centralized approaches, in this study the control perspective is moved from the system level to the single prosumer’s level, who is allowed to independently provide flexible power profiles through the aggregation of multiple DERs. A generalized optimization model, formulated as a Mixed-Integer Linear Programming (MILP) problem, is also introduced. Such a model is able to compute the optimal scheduling of a prosumer’s assets by considering both DR requests and end-users’ requirements in terms of comfort levels while minimizing the costs.

Machine learning and optimization for production rescheduling in Industry 4.0

Abstract: Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia

Abstract: Energy security and environmental sustainability have become an integral policy agenda worldwide whereby the global economic growth policies are being restructured to ensure the reliability of energy supply and safeguard environmental well-being as well However, technological inefficiency is one of the major hindrances in attaining these over-arching goals Hence, this paper probed into the non-linear impacts of ICT trade on the prospects of undergoing renewable energy transition, improving energy use efficiencies, enhancing access to cleaner cooking fuels, and mitigating carbon dioxide emissions across selected South Asian economies: Bangladesh, India, Pakistan, Sri Lanka, Nepal, and Maldives The results from the econometric analyses reveal that ICT trade directly increases renewable energy consumption, enhances renewable energy shares, reduces intensity of energy use, facilitates adoption of cleaner cooking fuels, and reduces carbon-dioxide emissions Moreover, ICT trade also indirectly mitigates carbon-dioxide emissions through boosting renewable energy consumption levels, improving energy efficiencies, and enhancing cleaner cooking fuel access Hence, these results, in a nutshell, portray the significance of reducing the barriers to ICT trade with respect to ensuring energy security and environmental sustainability across South Asia Therefore, it is ideal for the government to gradually lessen the trade barriers to boost the volumes of cross-border flows of green ICT commodities Besides, it is also recommended to attract foreign direct investments for the potential development of the respective ICT sectors of the South Asian economies

Balancing the economic and environmental performance of maritime transportation (vol 15, pg 458, 2010)

Abstract: This paper looks at the implications of various maritime emissions reductions policies for maritime logistics. There can be important trade-offs that have to be made between the environmental benefits associated with such measures as reduction in steaming speed and change in the number of vessels in the fleet, and more conventional logistics attributes such as in-transit inventory holdings.

Evaluating the factors that influence blockchain adoption in the freight logistics industry

Abstract: This study proposes a technology- organization- environment (TOE) theoretical framework of critical factors that influence the successful adoption of blockchain technologies in the freight logistics industry and prioritize them using the analytic network process (ANP). The research findings indicate that ‘availability of specific blockchain tools’, ‘infrastructural facility’, and ‘government policy and support’ are the three topmost ranked significant factors that influence the adoption of blockchains in the freight logistics industry. These findings will aid government agencies, freight logistics firms and blockchain service providers in strategizing for the advancement and successful adoption of blockchain and improvement of overall organizational competitiveness.

Advances in engineering software

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